用瞳孔放大和注视速度判断用户对主题熟悉度和问题具体性。
Characterising Topic Familiarity and Query Specificity Using Eye-Tracking Data
- 仅用瞳孔变化和注视速度，不依赖上下文推断认知状态。
- 预测主题熟悉度的宏平均F1达71.25%，问题具体性达60.54%。
- 为问答场景设计新标注规范，支持精准分类查询类型。
眼动数据已被证明与用户的知识水平和提问行为相关。以往研究多依赖注视点分析注意力，常需额外上下文信息。本研究则聚焦记忆相关的认知维度，仅通过瞳孔扩张和注视速度，无需任何上下文即可推断用户对主题的熟悉程度和问题的具体性。基于实验室用户研究（N=18）收集的眼动数据，使用梯度提升分类器预测主题熟悉度，获得71.25%的宏平均F1；使用k近邻（KNN）分类器预测问题具体性，获得60.54%的宏平均F1。此外，我们开发了一套专为问答任务设计的新标注指南，用于手动将查询分为具体或非具体两类。该研究验证了眼动数据在理解搜索中主题熟悉度与查询具体性的可行性。
原文摘要 · Abstract (English)
Eye-tracking data has been shown to correlate with a user's knowledge level and query formulation behaviour. While previous work has focused primarily on eye gaze fixations for attention analysis, often requiring additional contextual information, our study investigates the memory-related cognitive dimension by relying solely on pupil dilation and gaze velocity to infer users' topic familiarity and query specificity without needing any contextual information. Using eye-tracking data collected via a lab user study (N=18), we achieved a Macro F1 score of 71.25% for predicting topic familiarity with a Gradient Boosting classifier, and a Macro F1 score of 60.54% with a k-nearest neighbours (KNN) classifier for query specificity. Furthermore, we developed a novel annotation guideline -- specifically tailored for question answering -- to manually classify queries as Specific or Non-specific. This study demonstrates the feasibility of eye-tracking to better understand topic familiarity and query specificity in search.
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